Project about a model and a Dashboard focused on noise recognition.
The idea of this project is to create a model that is capable of recognizing the different sounds in an environment. In order to find this model, different models will be used, looking for the one that works best, at the same time the TensorFlow library will be used to create a neural network and compare if the model is better than said network. On the other hand it will be use a dashboard for deploying the model and neural network.
In this document we will see the progress and future ideas of the project, this also means that the project is still in development
This dataset contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The classes are drawn from the urban sound taxonomy. For a detailed description enter the following links:
- https://urbansounddataset.weebly.com/urbansound8k.html
- http://www.justinsalamon.com/uploads/4/3/9/4/4394963/salamon_urbansound_acmmm14.pdf
The Isolated urban sound database contains the audio samples used to design urban sound mixtures using SimScene software. https://zenodo.org/record/1213793#.YiYcAHrMKUl
The CitySounds2017train dataset comprising 1100 1-minute .wav audio files recorded at 44 green infrastructure sites within Greater London, UK between 2013 and 2015.
librosa is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems.
TensorFlow is an end-to-end open source platform for machine learning
Keras is TensorFlow's high-level API for building and training deep learning models. It is used for rapid prototyping, cutting-edge (state-of-the-art) research, and in production.
SoundDevice is a Python module that provides bindings for the PortAudio library and a few convenience functions to play and record NumPy arrays containing audio signals.
Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
Numpy is the fundamental package for scientific computing with Python
As said before, the main idea was to create a model that was capable of recognizing urban sounds. To achieve this, 3 datasets were implemented. Next, we will see some problems and the treatment given to each one.
Urban Sound 8K: This dataset contains 8732 labeled in 10 classes from Urban sounds.
Isolated Urban Sound and CitySounds2017train: This audio files were used to create the eleventh class named background.
To implement correctly the labeling it was created 2 functions on python:
This function uses the librosa library to load a single file and transforms it with a method
def extractor(file):
audio, sample_rate = librosa.load(file, res_type= "kaiser_fast")
mfccs_transformed = librosa.feature.mfcc(y=audio, sr = sample_rate, n_mfcc=50)
mfccs_scaled = np.mean(mfccs_transformed.T, axis=0)
return mfccs_scaled
This function uses the extractor function, the Urban Sound8k csv, the Isolated Urban sound and the CitySounds2017train to label the files with the 11 desired categories and transforms it
def feature_creator(path_1= noise_path,path_2=background_path,df=noise_dataset):
features = []
for index_num,row in df.iterrows():
file_name = os.path.join(os.path.abspath(path_1),'fold'+str(row["fold"])+'/',str(row["slice_file_name"]))
final_class_labels=row["class"]
data= extractor(file_name)
features.append([data,final_class_labels])
for elem in os.listdir(path_2):
file = os.path.join(os.path.abspath(path_2),elem)
mccfs = extractor(file)
features.append([mccfs, "Background"])
print(f"{len(features)} sounds loaded")
return features
After this the dataset was created and divided between X and y, also the pandas librarie was implemented to create categorie classes in the y.
Last but not least the X and y was divided with the sklearn librarie on train and test.
This was a multi class classification problem so it was needed a model capable of doing the classification, like the KNeighborsClassifier or the RandomForestClassifier
For the implementation of a neural network it was decided to use a sequential model with 4 layers, the first 3 with 750 neurons and the final with the 11 possible layers
Model: "sequential_9"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_37 (Dense) (None, 750) 38250
activation_37 (Activation) (None, 750) 0
dropout_28 (Dropout) (None, 750) 0
dense_38 (Dense) (None, 750) 563250
activation_38 (Activation) (None, 750) 0
dropout_29 (Dropout) (None, 750) 0
dense_39 (Dense) (None, 750) 563250
activation_39 (Activation) (None, 750) 0
dropout_30 (Dropout) (None, 750) 0
dense_40 (Dense) (None, 11) 8261
activation_40 (Activation) (None, 11) 0
It was implemented the CategoricalCrossentropy in the compilation of the model with the Adam optimizer.
Model training and saving
It was needed a recording function to get real time predictions so it was implemented 3 functions with the sounddevice libraries
Function that reads the audio
def read_audio(file):
with open(file, "rb") as audio_file:
audio_bytes = audio_file.read()
return audio_bytes
Function that records the audio using sounddevice
def record(duration=5, fs=48000):
sd.default.samplerate = fs
sd.default.channels = 1
sd.default.device = 0
myrecording = sd.rec(int(duration * fs))
sd.wait(duration)
return myrecording
Function that saves the recorded audio with wavio
def save_record(path_myrecording, myrecording, fs):
wavio.write(path_myrecording, myrecording, fs, sampwidth=2)
return None
- Audio samples (short audios, similiar sounds, more sounds needed)
- More classes to identify new environments
- Implementation of new audio file and features to get a better model.
- Implementation of new classes to recognise the situations and the other environments
- Web real time predictions.
$git init
$git clone https://github.com/Fedetorres210/Noise-recognition.git
$ pip install -r requirements.txt
It's necesary to have installed all the libraries of the requirements.txt file. Once have installed all, the server can be started.
To run the dashboard, in the same level where you have the file main.py , the next command:
streamlit run main.py
For this project is posible to create a image of docker on your computer and run it on local host.
First of all its necesary to have docker installed on your computer and running.
$docker ps
Now you have to go to the dashboard file and run the following commands:
$Docker build -t streamlitdashboard:latest .
$docker run -p 8501:8501 streamlitdashboard:latest
http://sedici.unlp.edu.ar/bitstream/handle/10915/23897/Documento_completo.pdf?sequence=1
https://github.com/krishnaik06/Audio-Classification
- McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. “librosa: Audio and music signal analysis in python.” In Proceedings of the 14th python in science conference, pp. 18-25. 2015.
- Justin Salomon - [email protected]
- Fairbrass, Alison (2018): CitySounds2017train audio files. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5886532.v1
- Rodríguez, Yohanna; Ballesteros L, Dora Maria; Renza, Diego (2019), “Fake voice recordings (Imitation)”, Mendeley Data, V1, doi: 10.17632/ytkv9w92t6.1
- Krishnaik: https://github.com/krishnaik06
- Fairbrass, Alison (2018): CitySounds2017train audio files. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5886532.v1
- Federico Torres Lobo - Creator
